Colposcopists' agreement on cervical biopsy site.
Bibliographic record
Abstract
OBJECTIVE: To determine the inter-observer agreement among colposcopists on the most abnormal area of the cervix from which a biopsy would be obtained and whether any attributes predict agreement. MATERIAL AND METHODS: Fifty cervigrams were reviewed and 72 colposcopists from five countries indicated the site to biopsy and whether an ECC should be obtained. Prior to the study, six Canadian colposcopists met to achieve consensus on the most diseased area for biopsy. Consensus was also reached on whether an ECC was indicated. For each cervigram, percent agreement was determined between each study colposcopist and the consensus. Data were analyzed to determine the attributes associated with the consensus response. RESULTS: The percent overall agreement of the colposcopists with the consensus diagnoses had a mean of 0.70 (95% CI, 0.65-0.75). The use of ECC was most common in Canada (15% of cases). The following factors were assessed by multivariate analysis to determine their influence on individual agreement with the consensus recommendation for the site to biopsy: country, duration of practice (less than or greater than 1 year), professional group (nurse, family doctor, pathologist, gynecologist, gynecologic oncologist), expert status (recognized national/international expert vs colposcopist), and gender. No factor was significantly associated. CONCLUSION: This international study was feasible and the level of inter-observer agreement among colposcopists on the location of the most severe lesions in cervical images is good.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".